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DAseq_covidChua.R
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DAseq_covidChua.R
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### DA-seq on COVID-19 data
### Original paper: https://www.nature.com/articles/s41587-020-0602-4
### This script reproduces analysis presented in Figure 4
library(Seurat) #V3
library(DAseq)
library(Matrix)
library(reshape2)
library(ggplot2)
library(cowplot)
source("convenience.R")
## Set Python and GPU
python2use <- "/data/henry/henry_env/venv/bin/python"
GPU <- 3
## Set path for FIt-SNE R wrapper
fitsneR <- "~/git/FIt-SNE/fast_tsne.R"
##=============================================##
## Data
## Load data
main_S <- readRDS(url("https://ndownloader.figshare.com/files/22927382"))
DefaultAssay(main_S) <- "integrated"
head([email protected])
table([email protected]$severity)
# check commanes
names(main_S@commands)
## Prepare object according to commands
# analysis
main_S <- ScaleData(main_S)
main_S <- RunPCA(
main_S, npcs = 90, verbose = F
)
main_S <- runFItSNE(
main_S, dims.use = 1:90, seed.use = 3, fast.R.path = fitsneR,
ann_not_vptree = FALSE, nthreads = 12
)
TSNEPlot(main_S, group.by = "severity")
TSNEPlot(main_S, group.by = "celltype", label = T)
## Separate immune cells from patient samples
# get immune cells
sort(unique([email protected]$celltype))
epi_type <- c("Basal","Ciliated","Ciliated-diff","FOXN4","Ionocyte","IRC",
"Secretory","Secretory-diff","Squamous","outliers_epithelial","unknown_epithelial")
immune_type <- setdiff(sort(unique([email protected]$celltype)), epi_type)
immune_S <- subset(x = main_S, cells = which([email protected]$celltype %in% immune_type))
# remove control cells
immune_S <- subset(immune_S, cells = which([email protected]$severity != "control"))
[email protected]$severity <- factor(as.character([email protected]$severity))
TSNEPlot(immune_S, group.by = "severity")
TSNEPlot(immune_S, group.by = "celltype", label = T)
# assign a number label for each cell type
[email protected]$celltype_num <- as.numeric(factor([email protected]$celltype))
##=============================================##
## DA-seq on immune cells
## Get sample labels
table([email protected]$patient)
table([email protected]$sample)
moderate_labels <- unique([email protected]$sample[[email protected]$severity == "moderate"])
critical_labels <- unique([email protected]$sample[[email protected]$severity == "critical"])
## DA cells
da_cells_immune <- getDAcells(
X = immune_S@[email protected],
cell.labels = [email protected]$sample,
labels.1 = moderate_labels, labels.2 = critical_labels,
k.vector = seq(200,3200,200),
plot.embedding = immune_S@[email protected],
)
da_cells_immune <- updateDAcells(
da_cells_immune, pred.thres = c(-0.8,0.8),
plot.embedding = immune_S@[email protected], size = 0.01
)
da_cells_immune$pred.plot
da_cells_immune$da.cells.plot
## DA regions
da_regions_immune <- getDAregion(
X = immune_S@[email protected], da.cells = da_cells_immune,
cell.labels = [email protected]$sample,
labels.1 = moderate_labels, labels.2 = critical_labels,
resolution = 0.01,
plot.embedding = immune_S@[email protected], size = 0.1
)
da_regions_immune$da.region.plot
da_regions_immune$DA.stat
n_da_immune <- length(unique(da_regions_immune$da.region.label)) - 1
## Local markers
# set cluster overlap for each DA
da_clusters_immune <- c(
"1" = "Neu", "2" = "nrMa", "4" = "CTL", "5" = "Neu"
)
# Seurat negbinom to find local markers
immune_S <- addDAslot(immune_S, da.regions = da_regions_immune)
local_markers_immune <- list()
for(i in 1:n_da_immune){
if(!as.character(i) %in% names(da_clusters_immune)){next()}
local_markers_immune[[as.character(i)]] <- SeuratLocalMarkers(
object = immune_S, da.region.to.run = i, cell.label.slot = "celltype", cell.label.to.run = da_clusters_immune[as.character(i)],
assay = "RNA", test.use = "negbinom", min.diff.pct = 0.09, only.pos = T
)
local_markers_immune[[as.character(i)]]$pct.diff <- local_markers_immune[[as.character(i)]]$pct.1 -
local_markers_immune[[as.character(i)]]$pct.2
}
lapply(c(1,2,4,5), function(x){
write.table(
local_markers_immune[[as.character(x)]], paste0("markers/covidChua_local_DA",x,".txt"),
sep = "\t", quote = F, row.names = T, col.names = T
)
})
##=============================================##
## Data from Liao et al.
## https://www.nature.com/articles/s41591-020-0901-9
## Download
liao_S <- readRDS(url("http://cells.ucsc.edu/covid19-balf/nCoV.rds"))
## Set clusters
epi_clusters <- c(13,16,25,28,31)
macro_clusters <- as.character(c(0,1,2,3,4,5,7,8,10,11,12,18,21,22,23,26))
t_clusters <- as.character(c(6,9,14))
cd8t_clusters <- as.character(c(9,14))
## Calculate module score
liao_immune_S <- subset(liao_S, cells = which([email protected]$cluster %in% epi_clusters & liao_S$group != "HC"))
[email protected]$severity <- gsub("O", "moderate", [email protected]$group1)
[email protected]$severity <- gsub("S/C", "critical", [email protected]$severity)
da_gene_modules <- lapply(local_markers_immune, FUN = function(x){
rownames(x)[1:min(100,nrow(x))]
})
names(da_gene_modules) <- paste0("DA", names(da_gene_modules))
liao_immune_S <- AddModuleScore(
object = liao_immune_S, features = da_gene_modules, assay = "RNA", name = names(da_gene_modules)
)
for(i in 1:4){
colnames([email protected])[grep(names(da_gene_modules)[i],colnames([email protected]))] <-
names(da_gene_modules)[i]
}
##=============================================##
## Generate plots
library(scales)
da_cols <- hue_pal()(n_da_immune)
da_order <- order(da_regions_immune$da.region.label)
tsne_embedding <- immune_S@[email protected]
## TSNE plots
gg1 <- plotCellLabel(
tsne_embedding, label = [email protected]$severity, size = 0.01, do.label = F
) + theme_tsne
ggsave(gg1, filename = "figs/covidChua_a.png", width = 50, height = 50, units = "mm", dpi = 1200)
ggsave(g_legend(gg1, legend.position = "right"),
filename = "figs/covidChua_a_legend.pdf", width = 0.7, height = 0.3, dpi = 1200)
gg2 <- plotCellLabel(
tsne_embedding, label = factor([email protected]$celltype_num),
size = 0.01, label.size = 2
) + scale_color_hue(labels = paste(sort(unique([email protected]$celltype_num)), immune_type, sep = "-")) + theme_tsne
ggsave(gg2, filename = "figs/covidChua_b.png", width = 50, height = 50, units = "mm", dpi = 1200)
ggsave(g_legend(gg2), filename = "figs/covidChua_b_legend.pdf", width = 1, height = 1.6, dpi = 1200)
gg3 <- da_cells_immune$pred.plot + theme_tsne
ggsave(gg3, filename = "figs/covidChua_c.png", width = 50, height = 50, units = "mm", dpi = 1200)
ggsave(g_legend(gg3, legend.key.height = unit(0.4,"cm"), legend.key.width = unit(0.4,"cm")),
filename = "figs/covidChua_c_legend.pdf", height = 30, width = 15, units = "mm", dpi = 1200)
gg4 <- plotCellLabel(
tsne_embedding[da_order,], label = as.character(da_regions_immune$da.region.label[da_order]),
size = 0.01, label.size = 2, label.plot = as.character(c(1:n_da_immune))
) + scale_color_manual(
values = c("gray", da_cols), breaks = c(1:n_da_immune), labels = paste0("DA",c(1:n_da_immune))
) + theme_tsne
ggsave(gg4, filename = "figs/covidChua_d.png", width = 50, height = 50, units = "mm", dpi = 1200)
ggsave(g_legend(gg4), filename = "figs/covidChua_d_legend.pdf", width = 0.5, height = 0.8, dpi = 1200)
## Dot plot
marker_genes <- list(
"1" = c("CXCR4","CD63","CD48","IRAK3"),
"2" = c("RGL1","MAFB","MAF","SIGLEC1"),
"4" = c("IFNG","CCR6","TOX2","IL26"),
"5" = c("IL1RN","SOCS3","PTGS2","IL1B")
)
DefaultAssay(immune_S) <- "RNA"
gg5 <- DotPlot(
immune_S, features = unlist(marker_genes), cols = c("gray","blue"), group.by = "da"
) + theme_dot + RotatedAxis() + guides(
color = guide_colorbar(title = "Average Expression", order = 2),
size = guide_legend(title = "Percent Expressed", order = 1)
)
ggsave(gg5, filename = "figs/covidChua_e.pdf", width = 90, height = 40, units = "mm", dpi = 1200)
ggsave(
g_legend(gg5, legend.key.height = unit(0.15,"cm"), legend.key.width = unit(0.2,"cm"), legend.spacing = unit(0.25, 'cm')),
filename = "figs/covidChua_e_legend.pdf", height = 50, width = 30, units = "mm", dpi = 1200
)
for(i in 1:n_da_immune){
if(!as.character(i) %in% names(da_clusters_immune)){next}
[email protected]$da.local <- "0"
[email protected]$da.local[[email protected]$celltype == da_clusters_immune[as.character(i)]] <-
da_clusters_immune[as.character(i)]
[email protected]$da.local[[email protected]$da == i] <- paste0("DA",i)
[email protected]$da.local <- factor([email protected]$da.local, levels = c("0",da_clusters_immune[as.character(i)],paste0("DA",i)))
gg1 <- DotPlot(
immune_S, features = marker_genes[[as.character(i)]], group.by = "da.local", cols = c("gray","blue")
) + theme_dot + theme(axis.text.x = element_blank())
ggsave(gg1, filename = paste0("figs/covidChua_g_DA",i,".pdf"), width = 25, height = 25, units = "mm", dpi = 1200)
[email protected]$da.local <- NULL
}
DefaultAssay(immune_S) <- "integrated"
## Feature plots
# DA1
sgg1 <- c(
list(plotCellLabel(
tsne_embedding[da_order,], as.factor(da_regions_immune$da.region.label[da_order]), size = 0.01, do.label = F,
cell.col = c("gray",da_cols[1],"gray","gray","gray","gray")
) + ggtitle("DA1") + theme_tsne),
lapply(c("CD48","CD63","CXCR4"), FUN = function(x){
plotCellScore(
tsne_embedding, immune_S@assays$RNA@data[x,], cell.col = c("gray","blue"), size = 0.01
) + ggtitle(x) + theme_tsne
})
)
ggsave(
plot_grid(plotlist = sgg1, nrow = 1),
filename = "figs/covidChua_s_a.png", height = 45, width = 160, units = "mm", dpi = 1200
)
# DA5
sgg2 <- c(
list(plotCellLabel(
tsne_embedding[da_order,], as.factor(da_regions_immune$da.region.label[da_order]), size = 0.01, do.label = F,
cell.col = c("gray","gray","gray","gray","gray",da_cols[5])
) + ggtitle("DA5") + theme_tsne),
lapply(c("IL1RN","SOCS3","PTGS2"), FUN = function(x){
plotCellScore(
tsne_embedding, immune_S@assays$RNA@data[x,], cell.col = c("gray","blue"), size = 0.01
) + ggtitle(x) + theme_tsne
})
)
ggsave(
plot_grid(plotlist = sgg2, nrow = 1),
filename = "figs/covidChua_s_b.png", height = 45, width = 160, units = "mm", dpi = 1200
)
# DA2
sgg3 <- c(
list(plotCellLabel(
tsne_embedding[da_order,], as.factor(da_regions_immune$da.region.label[da_order]), size = 0.01, do.label = F,
cell.col = c("gray","gray",da_cols[2],"gray","gray","gray")
) + ggtitle("DA2") + theme_tsne),
lapply(c("RGL1","MAFB","SIGLEC1"), FUN = function(x){
plotCellScore(
tsne_embedding, immune_S@assays$RNA@data[x,], cell.col = c("gray","blue"), size = 0.01
) + ggtitle(x) + theme_tsne
})
)
ggsave(
plot_grid(plotlist = sgg3, nrow = 1),
filename = "figs/covidChua_s_c.png", height = 45, width = 160, units = "mm", dpi = 1200
)
# DA4
sgg4 <- c(
list(plotCellLabel(
tsne_embedding[da_order,], as.factor(da_regions_immune$da.region.label[da_order]), size = 0.01, do.label = F,
cell.col = c("gray","gray","gray","gray",da_cols[4],"gray")
) + ggtitle("DA4") + theme_tsne),
lapply("IFNG", FUN = function(x){
plotCellScore(
tsne_embedding, immune_S@assays$RNA@data[x,], cell.col = c("gray","blue"), size = 0.01
) + ggtitle(x) + theme_tsne
})
)
ggsave(
plot_grid(plotlist = sgg4, nrow = 1),
filename = "figs/covidChua_s_d.png", height = 45, width = 80, units = "mm", dpi = 1200
)
## Violin plots of gene module scores for Liao's data
gg6 <- list(
# neutrophil, DA1&DA5, C15
VlnPlot(
subset(liao_immune_S, cells = which(liao_immune_S$cluster == "15")),
features = "DA1", group.by = "severity", pt.size = 0
) + theme_tsne,
VlnPlot(
subset(liao_immune_S, cells = which(liao_immune_S$cluster == "15")),
features = "DA5", group.by = "severity", pt.size = 0
) + theme_tsne,
# macrophage, DA2
VlnPlot(
subset(liao_immune_S, cells = which(liao_immune_S$cluster %in% macro_clusters)),
features = "DA2", group.by = "severity", pt.size = 0
) + theme_tsne,
# CTL, DA4
VlnPlot(
subset(liao_immune_S, cells = which(liao_immune_S$cluster %in% cd8t_clusters)),
features = "DA4", group.by = "severity", pt.size = 0
) + theme_tsne
)
ggsave(
plot_grid(plotlist = gg6, nrow = 2),
filename = "figs/covidChua_s_e.pdf", height = 45, width = 45, units = "mm", dpi = 1200
)
# p-value
sapply(c(1,2,4,5), function(x){
if(x == 1 | x == 5){
# neutrophil, DA1&DA5, C15
liao_sub_S <- subset(liao_immune_S, cells = which(liao_immune_S$cluster == "15"))
} else if(x == 2){
liao_sub_S <- subset(liao_immune_S, cells = which(liao_immune_S$cluster %in% macro_clusters))
} else if(x == 4){
liao_sub_S <- subset(liao_immune_S, cells = which(liao_immune_S$cluster %in% cd8t_clusters))
}
print(table([email protected]$severity))
print(wilcox.test(x = [email protected][which([email protected]$severity == "critical"),paste0("DA",x)],
y = [email protected][which([email protected]$severity == "moderate"),paste0("DA",x)])$p.value)
})